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Pro Observer
A human crowd pattern discovery engine using AI models to analyze pose keypoint sequences in real-time camera feeds for action estimation and classification.
Client
Pro Series, LLC.
Date
January 10, 2021
Service
Machine Learning Engineering
Location
San Juan, PR (Remote)
Challenges
- Data Quality: Obtaining accurate and diverse pose keypoint data for training reliable models.
- Real-time Processing: Ensuring low-latency processing for analyzing continuous camera feeds.
- Model Complexity: Managing multiple AI models and their interactions for accurate action classification.
- Scalability: Scaling the solution to handle large crowds and multiple camera feeds simultaneously.
- Privacy Concerns: Addressing privacy issues related to capturing and analyzing human actions in public spaces.
- Hardware Requirements: Requiring powerful hardware for efficient inference, especially in real-time scenarios.
- Algorithm Optimization: Optimizing algorithms for speed and accuracy, especially when dealing with complex crowd patterns.
- Ethical Implications: Considering the ethical use of the technology, avoiding biases, and ensuring fairness in classifications.
- Adaptability: Building a system that can adapt to different environments and lighting conditions for accurate pose detection.
- Regulatory Compliance: Adhering to regulations and legal requirements related to data privacy and surveillance technologies.
Project Results
To overcome the challenges in building a human crowd pattern discovery engine, a comprehensive approach was taken. Firstly, a meticulous data collection strategy ensured a diverse and high-quality dataset, crucial for training the AI models. Real-time processing hurdles were tackled through the utilization of high-performance computing infrastructure and optimized algorithms, enabling swift analysis of continuous camera feeds. The complexity of multiple AI models was managed by developing an efficient inference pipeline, ensuring seamless interaction between models for accurate action classification. Scalability was achieved through a distributed computing architecture, allowing the system to handle large crowds and numerous camera feeds simultaneously. Addressing privacy concerns, the system implemented strict anonymization protocols, guaranteeing individuals' identities remained confidential during analysis. Ethical implications were mitigated by employing algorithms designed to recognize and rectify biases, ensuring fairness in classifications. Moreover, constant adaptation and refinement were prioritized, enabling the system to adjust to diverse environments and lighting conditions. Regulatory compliance was ensured by adhering to stringent data privacy regulations and collaborating closely with legal experts. Through this meticulous process, a robust and ethical crowd engine was successfully developed, capable of analyzing intricate movement patterns and actions on video feeds with precision and reliability.